Engineering-approach accelerates computational understanding of V1–V2 neural properties
Engineering-approach accelerates computational understanding of V1–V2 neural properties
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DOI：
10.1007/s11571-008-9065-x
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发表时间：
2009-03
影响因子：
3.7
通讯作者：
Shunji Satoh;S. Usui
中科院分区：
文献类型：
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作者：
Shunji Satoh;S. Usui
我们提出了两个计算模型（i）长距离水平连接和V1的非线性效应和（ii）在盲点的填充过程。这两个模型都是从标准正则化理论推导得到的，表明V1和V2神经特性的生理证据对于有效的图像处理是必不可少的。我们强调，工程的方法应该进口来理解视觉系统计算，即使这种方法通常忽略生理证据和目标既不是神经元也不是大脑。
We present two computational models (i) long-range horizontal connections and the nonlinear effect in V1 and (ii) the filling-in process at the blind spot. Both models are obtained deductively from standard regularization theory to show that physiological evidence of V1 and V2 neural properties is essential for efficient image processing. We stress that the engineering approach should be imported to understand visual systems computationally, even though this approach usually ignores physiological evidence and the target is neither neurons nor the brain.